ORIGINAL RESEARCH article
Front. Endocrinol.
Sec. Bone Research
Volume 16 - 2025 | doi: 10.3389/fendo.2025.1604133
Lightweight Deep Learning System for Automated Bone Age Assessment in Chinese Children: Enhancing Clinical Efficiency and Diagnostic Accuracy
Provisionally accepted
- Facilitate Healthy Developments for Children (Hebei) Technology Co., Ltd., Hebei, Shijiazhuang, 050000, China, Shijiazhuang, China
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Bone age assessment (BAA) is a critical diagnostic tool for evaluating skeletal maturity and monitoring growth disorders. Traditional clinical methods, however, are highly subjective, timeconsuming, and reliant on clinician expertise, leading to inefficiencies and variability in accuracy. To address these limitations, this study introduces a novel lightweight two-stage deep learning framework based on the Chinese 05 BAA standard. In the first stage, the YOLOv8 algorithm precisely localizes 13 key epiphyses in hand radiographs, achieving a mean Average Precision (mAP) of 99.5% at Intersection over Union (IoU) = 0.5 and 94.0% within IoU 0.5-0.95, demonstrating robust detection performance. The second stage employs a modified EfficientNetB3 architecture for fine-grained epiphyseal grade classification, enhanced by the Rectified Adam (RAdam) optimizer and a composite loss function combining center loss and weighted cross-entropy to mitigate class imbalance. The model attains an average accuracy of 80.3% on the training set and 81.5% on the test set, with a total parameter count of 15.8 million-56-86% fewer than comparable models (e.g., ResNet50, InceptionV3). This lightweight design reduces computational complexity, enabling faster inference while maintaining diagnostic precision. This framework holds transformative potential for pediatric endocrinology and orthopedics by standardizing BAA, improving diagnostic equity, and optimizing resource use. Success hinges on addressing technical, ethical, and adoption challenges through collaborative efforts among developers, clinicians, and regulators. Future directions might include multimodal AI integrating clinical data (e.g., height, genetics) for holistic growth assessments.
Keywords: Chinese 05, Bone age assessment, Lightweight deep neural network, YOLOv8, EfficientNetB3
Received: 02 Apr 2025; Accepted: 11 Jun 2025.
Copyright: © 2025 Hai, Bin, Kexing, Cong and Fei. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Zhang Bin, Facilitate Healthy Developments for Children (Hebei) Technology Co., Ltd., Hebei, Shijiazhuang, 050000, China, Shijiazhuang, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.